It has always been difficult for advertisers to gauge the effectiveness of their advertisements particularly where the advertising is done through traditional modes of advertising such as television or newspaper. Generally speaking, television networks and newspaper publishers have only approximate statistics on the number of viewers or readers within a given market. Newspaper publishers, for example, can approximate the number of newspapers that are read on any given day based on subscription and other sales data. Of course, every person who receives a newspaper is not going to read every advertisement within that paper. Consequently, newspaper publishers and those who purchase advertisements from the publishers have only a loose idea of how many people are exposed to or actually read their advertisements. Likewise with television advertising, the viewership of any given program, and the commercials that run during such programs, is not known with precision. The so-called ‘ratings’ for television programs are gathered statistically and again, calculating the number of people who are reached with any given advertisement is imprecise. Ideally, advertisers would like more substantive feedback about who and how their advertising content is being viewed.
With the rise of Internet advertising, advertisers are given more direct and immediate feedback on who is viewing their advertisements. Suppose, for example, that an advertiser purchases advertisements on the website of a major internet search engine such as Google. The advertisement provider, Google in this case, gathers data on the precise number of times that a given advertisement is actually rendered during a page view. Likewise, the advertisement provider can gather data representing the precise number of times a given advertisement is actually clicked by the viewer of the advertisement. Such feedback is invaluable because it allows advertisers to get feedback on the exact, rather than approximate, number of impressions the advertising made on the target audience. An ‘impression’ is any exposure a person has to an advertisement. In the context of a newspaper, an advertisement has an impression every time a person turns to the page of the paper where the advertisement is located. Since it is not possible to know with any certainty what pages of a newspaper are every actually viewed by a person, it is not possible to know with any certainty how many impressions a newspaper-based advertisement receives. A similar problem exists with television advertising because, as was discussed above, television ‘ratings’ are statistical estimates and calculating the number of people reached with any given advertisement is imprecise.
In addition to impression information, the feedback provided by an internet advertisement provider such as Google also provides valuable information about how effective an internet-based advertisement is in generating an inquiry (i.e. it tells you how many impressions actually result in a click on the advertisement). Data generated by, and fed back from, an advertising channel is more commonly known as ‘back-channel data.’ Back-channel data has increasingly become the currency driving Internet advertising business. Absolute measurement—vs. statistical analysis—is key to advertisers, corporate and content programmer confidence.
Although television, newspaper and magazine advertising channels continue to be very important, other forms of advertising such as audio, video and electronic signage in retail spaces, hotels, restaurants and other public places are becoming increasingly prevalent. Such advertising media might comprise playback of DVD's, computer generated media or animation, set-top box video and audio, satellite dish video, streaming internet protocol television (‘IPTV’), still pictures, or even audio. Some such systems have the capability to report on what media content was played at what time and to schedule the time at which particular media is played. While these are very valuable controls for advertisers who wish to control their messaging, there is currently no mechanism for reporting how many people were or are exposed to an impression of such media content. Likewise, there is no mechanism for adapting the media content to account for local variables and conditions detected during media playback.
There is therefore a need for an media delivery system that gathers data about the number and type of human impressions of media content delivered by a content rendering device for cross-correlation of such impression data with the media content. Such a system may also alter the media content it delivers based on such data.